Medical images play an indispensable role in clinical diagnosis. Most of the images, after being acquired, need to be examined and labeled by a radiologist. The radiologist then provides a pathological report to physicians. Due to the high volume of images that need to be examined, most hospitals or Internet-based remote diagnosis platforms use queuing systems to help manage the images. Radiologists may select medial images for processing based on the order in which they appear in the queue).
In general, a traditional system may sort patient cases according to the acquisition time of their respective medical images. Sorting the patient cases in this manner enables the patients to be examined on a first come first serve basis. Alternatively, a system may comprehensively consider the data acquisition time, the urgency of a disease and the workload of doctors to reasonably schedule and distribute the medical related data. For example, cerebral hemorrhage and cerebral thrombosis are two common acute diseases. Although both are cerebral blood diseases, the treatment methods of the two diseases are completely different. If the doctor can make a correct diagnosis within hours of illness and treat the patient accordingly, the risk of the diseases can be significantly reduced, and the rehabilitation of the patient can be improved. For such images, the queuing management system may assign a higher priority, place them at the top of the queue, and label the patient cases as emergency cases. If an emergency case that requires immediate processing enters into the queue, a radiology may rearrange non-emergency works at hand and handle the emergency case immediately.
It is noted that the priorities of the medical images are typically derived from the priority of the disease conditions of the patient cases. The priority of a disease condition is typically decided manually by a physician handling the case (e.g., he may determine the condition based on the symptoms of the patient). Such a determination is vulnerable to diagnosis errors (e.g., the physician may misdiagnose the seriousness of a patient's condition). Consequently, priorities of certain cases may be underestimated, and the optimal treatment time window may be delayed. Moreover, physicians often need to refer to examination results provided by radiologists in order to make informed decisions. Because medical images of certain cases may need to be sorted before examination results are made available, the sorting performed in such cases may not be meaningful. Furthermore, radiologists tasked to sort the medical images may be overloaded with other tasks, which may lead to additional human errors, further reducing the accuracy and efficiency of the image management system.